CVIPtools

 A software package for the exploration of computer vision and image processing

One of the primary purposes of the CVIPtools development is to allow students, faculty, and other researchers to explore the power of computer processing of digital images.
Team lead by :: Dr. Scott E Umbaugh
Developed by:
Computer Vision and Image Processing Laboratory
Southern Illinois University @  Edwardsville
Illinois, USA

The new Windows version of CVIPtools, CVIPtools 5.x is implemented in four layers: the algorithms code layer, the Common Object Module (COM) interface layer, the CvipOp layer, and the Graphical User Interface (GUI).

    Brief list of the functionality currently available from the CVIPtools GUI:
          o Edge/Line detection and edge linking
          o Image segmentation - fuzzyc mean,  principal components transform/median cut,
          o Morphological filters -
          o Two-dimensional fast transforms - Fourier (FFT), cosine (DCT),
          o Frequency domain filters - highpass, lowpass, bandpass, bandreject,
          o Feature extraction - binary, RST-invariant, histogram,
          o Feature analysis - Range-Normalization, Unit Vector Normalization,
          o Pattern classification - nearest neighbor, K-nearest neighbor and nearest centroid.
          o Spatial domain image restoration - order filters, mean filters, adaptive filters.
          o Frequency domain image restoration filters - inverse, geometric mean,
          o Noise functions - negative exponential, rayleigh, gaussian,
          o Geometry transformation - user specified mesh. Nearest neighbor,
          o Histogram-based image enhancement - histogram equalization,
          o Pseudo-color enhancement - frequency domain mapping, gray level mapping,
          o Spatial and frequency domain image smoothing and sharpening.
          o Image compression algorithms - bitplane run-length-coding,
          o Support for common image formats - Sun Raster, IRIX, GIF, TIFF, JPEG,
          o Image geometry operations - copy-paste, translate, resize, rotate, zoom.
          o Utilities for arithmetic and logic operations, image file conversion,

CVIPTools based book:
Digital Image Processing and Analysis: Human and Computer Vision Applications with CVIPtools, Second Edition.
ISBN-10: 143980205X   ISBN-13: 978-1439802052 
Publication Year: 2010
CRC Press
Author:: Dr. Scott E Umbaugh
cost :: around 100$

I am not satisfied with the GUI of the tool. It is not very intuitive. I have not downloaded and used. Photo of the BOOK takes 2.5MB. Very few full text papers are there to download. 

Computational Photography

 Courtesy:: Economist

The principle of focusing rays through an aperture onto a two-dimensional surface remains the same from 5th century BC Camera obscura to latest digital camera.

Computational photography, a subdiscipline of computer graphics, conjures up images rather than simply capturing them.

The best example of computational photography is high-dynamic-range (HDR) imaging. It combines multiple photos shot in rapid succession, and at different exposures, into one single picture of superior quality. Apple added HDR as an option in the iPhone 4.

 HDR idea was developed by  Marc Levoy of Stanford University, with his colleague Pat Hanrahan in 1996. They wrote a journal paper that  described a way of simplifying ligh field mathematically. It is now a reality because "You are getting more computing power per pixel."

 Dr Levoy developed SynthCam and Frankencameras that can improve photos taken in low-light conditions. On June 22nd Ren Ng, a former student of his at Stanford, launched a new company called Lytro,  which promises to launch an affordable snapshot camera.

He used an approach is known as light-field photography, and Lytro's camera will be its first commercial exploration. In physics, a light field describes the direction of all the idealised light rays passing through an area. In Dr Ng's camera light field is created using an array of microlenses inserted in between an ordinary camera lens and the image sensor. By calculating the path between the lens and the sensor, the precise direction of a light ray can be reconstructed.

This ray tracing concept also derived from computer graphics. In computer graphics , the technique is used to paint realistic reflections of one artificial object on another, among other things.

for detailed and more information go to :: http://www.economist.com/node/21522976

DIA in Agriculture

Automated Rice Leaf Disease Detection Using Color Image Analysis

By R.A.D.L. Pugoy and V.Y. Mariano, in  3rd International Conference on Digital Image Processing (ICDIP 2011), Chengdu, China, 15-17 April 2011

Abstract
In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as manual eyeball exercise, is important to come up with good nutrient and disease management strategies.
In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based K-means clustering algorithm to group related regions into clusters. Then these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf.

Mariano Details
Vladimir Y. Mariano,

Associate Professor, Institute of Computer Science,

The University of the Philippines Los Baños.

 

Source:: http://www.ics.uplb.edu.ph/node/477

Document image analysis and recognition

By Simone Marinai 
Document image analysis and recognition (DIAR) is a research field that has its roots in the first Optical Character Recognition (OCR) systems, applied for reading numeric check codes. Nowadays, the technology related to DIAR is used in a broad range of applications, where some information has to be extracted from structured documents existing in different media. Typical applications include, among the others, handwritten character recognition, processing of textual web images, and information extraction from digital libraries. In the digital library community a lot of efforts have been devoted to the digitization of paper collections in order to archive them as document image collections. Large digital archives hare currently available, however their full fruition can be achieved only by accessing the information that is embedded in the digital image. The simple application of Optical Character Recognition (OCR) packages can only partially solve these problems, both for the difficulty of obtaining clean converted text and for the lack of structural description of the document. To tackle this problems either layout analysis methods or document image retrieval approaches can be considered.

Scanning and storage

• Raw storage
• Image compression
• Document image compression

Image pre-processing

• Noise removal
• Skew detection
• Connected components computation

Layout analysis

• Geometric and logic layout analysis
• Region segmentation and labeling
• Page classification

OCR and handwriting recognition

• Character segmentation
• Word recognition

Document image retrieval

• Processing of converted text
• Retrieval by layout similarity

http://www.dsi.unifi.it/~simone/DIAR/

Keith VisionBib, PEIPA, CV Homepage

The Computer Vision Homepage

     The Computer Vision Homepage was established at Carnegie Mellon University in 1994 to provide a central location for World Wide Web links relating to computer vision research. Due to the success of the concept, we have broken the original monolithic site into a number of specific subpages. The emphasis of the Computer Vision Homepage is on computer vision research rather than on commercial products.

http://www.cs.cmu.edu/~cil/vision.html

Pilot European Image Processing Archive

     PEIPA is an archive relating to image processing and analysis, with emphasis on computer vision. Its principal aim is to provide information, datasets and software that allow the effectiveness of algorithms to be measured and compared. This is known variously as performance characterization, performance estimation and benchmarking.

Only following two institutes are included in the list from India.

• Biomedical Lab, Indian Institute of Science, Bangalore
• Image processing,Vision and Soft Computing Laboratory, Indian Statistical Institute, Machine Intelligence Unit

 http://peipa.essex.ac.uk/

VisionBib.Com - Computer Vision Information Pages

The VisionBib.Com site is a collection of web sites that provide a variety of resources related, mostly, to the area of Computer Vision.  The Annotated Computer Vision Bibliography.With indexes for Author, Journal/Conference, Keyword, Words in title, and Authors. This bibliography first appeared on the internet in early 1994 and provides information on 124,000+ scientific papers in the field of computer vision, image processing, character recognition and other related topics. Many entries have links to online versions (111,000+), but most journals require one has to purchase the full text.

Conference Listing is available.
 

http://www.visionbib.com/